Summary of Functional Risk Minimization, by Ferran Alet et al.
Functional Risk Minimization
by Ferran Alet, Clement Gehring, Tomás Lozano-Pérez, Kenji Kawaguchi, Joshua B. Tenenbaum, Leslie Pack Kaelbling
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new framework for Machine Learning called Functional Risk Minimization (FRM), which compares functions rather than outputs to minimize risk. The authors argue that this approach leads to better performance in various experiments, including supervised learning, unsupervised learning, and reinforcement learning. FRM subsumes the traditional Empirical Risk Minimization (ERM) framework for many common loss functions and can capture more realistic noise processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about improving a fundamental principle of Machine Learning called Empirical Risk Minimization. The authors are proposing a new way to do this, called Functional Risk Minimization, which they think will work better in some situations. They show that their approach can lead to better results in different types of learning experiments. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Supervised » Unsupervised
